Nerve monitoring during electrosurgery

ABSTRACT

A signal processing module includes an input module electronically coupled to a sensing probe of a nerve integrity monitoring system. The probe senses electrical signals from a patient during operation of an electrosurgical unit. The input module receives an input signal from the probe. An EMG detection module is coupled to the input module and is adapted to detect conditions in the input signal. The conditions are classified as a function of a level of electromyographic activity. An output module, coupled to the EMG detection module, provides an indication of electromyographic activity in the input signal based on the detected conditions.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a Divisional Application of U.S. patent applicationSer. No. 12/363,154 having a filing date of Jan. 30, 2009, and which isincorporated herein by reference.

BACKGROUND

The present disclosure relates to a nerve monitoring system. Moreparticularly, it relates to monitoring nerve activity duringelectrosurgery or in the presence of electrical artifacts from metalsurgical instruments.

Electrophysiological monitoring assists a surgeon in locating nerveswithin an obscured surgical field, as well as preserving and assessingnerve function in real-time during surgery. To this end, nerve integritymonitoring is commonly employed to monitor electromyographic (EMG)activity. During nerve integrity monitoring, sensing or recordingelectrodes are coupled to appropriate tissue (e.g., cranial musclesinnervated or controlled by the nerve of interest, peripheral nerve,spinal cord, brainstem, etc.) to sense EMG activity. Stimulation, forexample electrical stimulation or mechanical stimulation, can causeexcitement of the tissue. During electrical stimulation, a stimulationprobe applies a stimulation signal near the area where the subject nervemay be located. If the stimulation probe contacts or is reasonably nearthe nerve, the applied stimulation signal is transmitted through thenerve to excite the innervated tissue. In mechanical stimulation, directphysical contact of the appropriate tissue can cause excitement of thetissue. In any event, excitement of the related tissue generates anelectrical impulse that is sensed by the recording electrodes (or othersensing device). The recording electrode(s) signal the sensed electricalimpulse information to the surgeon for interpretation in the context ofdetermining EMG activity. For example, the EMG activity can be displayedon a monitor and/or presented audibly.

Nerve integrity monitoring is useful for a multitude of differentsurgical procedures or evaluations that involve or relate to nervetissue, muscle tissue, or recording of neurogenic potential. Forexample, various head and neck surgical procedures require locating andidentifying cranial and peripheral motor nerves. In some instances, anelectrosurgical unit is used to perform these surgical procedures.Current electrosurgical units include a conductive tip or needle thatserves as one electrode in an electrical circuit which is completed viaa grounding electrode coupled to the patient. Incision of tissue isaccomplished by applying a source of electrical energy (most commonly, aradio-frequency generator to the tip). Upon application of the tip tothe tissue, a voltage gradient is created, thereby inducing current flowand related heat generation at the point of contact. With sufficientlyhigh levels of electrical energy, the heat generated is sufficient tocut the tissue and, advantageously, to simultaneously cauterize severedblood vessels.

Due to the levels of electrical energy generated by electrosurgicalunits, systems for nerve integrity monitoring experience a large amountof electrical interference when used during electrosurgical procedures.The electrical interference can create incorrect signals of EMG activity(e.g., false positives) as well as introduce a significant amount ofnoise in the nerve integrity monitoring system. As a result, currenttechniques involve using a probe to mute all channels of the nerveintegrity monitoring system during an electrosurgical procedure. As aresult, monitoring of EMG activity is suspended during operation of theelectrosurgical unit. In order for a surgeon to prevent cutting a nervewith the electrosurgical unit, the surgeon will cut for a brief periodand then stop cutting such that nerve integrity monitoring can berestored. If no EMG activity is detected, the surgeon can then cut foranother brief period, while pausing intermittently to restore nerveintegrity monitoring so as to prevent from cutting a nerve. This processis repeated until the surgeon is completed with the electrosurgicalprocedure. Without being able to monitor EMG activity during anelectrosurgical procedure, the electrosurgical procedure can becumbersome and time consuming.

SUMMARY

Concepts presented herein relate to a signal processing module, asurgical method and a nerve integrity monitoring system. An input moduleof the signal processing module is electronically coupled to a sensingprobe of the nerve integrity monitoring system. The probe senseselectrical signals from a patient during operation of an electrosurgicalunit. The input module receives an input signal from the probe. An EMGdetection module is coupled to the input module and is adapted to detectconditions in the input signal. The conditions are classified as afunction of a level of electromyographic activity. An output module,coupled to the EMG detection module, provides an indication of thedetected conditions.

An artifact detection module can also be employed to detect an artifactcondition in the input signal. The artifact detection module canestimate a power of the input signal to detect the artifact.Additionally, other modules can be included such as a direct currentfilter module and an EMG recovery module.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic block diagram of a surgical environment includinga nerve integrity monitoring system and an electrosurgical unit.

FIG. 2 is a schematic block diagram of a signal processing module in thenerve integrity monitoring system of FIG. 1.

FIG. 3 is a flow diagram of a method for processing signals in the nerveintegrity monitoring system of FIG. 1.

FIG. 4 is a flow diagram of a method for detecting an artifact in one ormore channels that provide input to the nerve integrity monitoringsystem of FIG. 1.

FIG. 5 is a flow diagram of a method for filtering a low frequency noisecomponent from one or more channels that provide data to the nerveintegrity monitoring system of FIG. 1.

FIG. 6 is a flow diagram of a method for detecting EMG activity from oneor more channels that provide input to the nerve integrity monitoringsystem of FIG. 1.

FIG. 7 is a flow diagram of a method for EMG signal recovery for one ormore channels that provide data to the nerve integrity monitoring systemof FIG. 1.

DETAILED DESCRIPTION

A surgical environment including a nerve integrity monitoring system 10and an electrosurgical unit (ESU) 12 is illustrated in FIG. 1. Ingeneral terms, the system 10 is configured to assist in and performnerve integrity monitoring for virtually any nerve/muscle combination ofthe human anatomy, as well as recording nerve potential. The system 10includes a control unit 20, which can assume a wide variety of forms andin one embodiment includes a console 30, having a monitor 32, and apatient interface module 40. The electrosurgical unit 12 includes an ESUgenerator 42 coupled to a surgical instrument 44. The ESU generator 42generates current that is sent to surgical instrument 44 for cutting orotherwise manipulating tissue of a patient.

System 10 includes a stimulation probe assembly 50, which can be usedfor electrical stimulation, as well as one or more sensing probes 52,which can be any type of sensing device such as an electrode. Thecontrol unit 20 facilitates operation of the probe assembly 50, as wellas processes all information generated by sensing probes 52 and othersystem 10 components (not shown) during use. The probe assembly 50 andthe control unit 20 are adapted to allow control and variation of astimulus energy delivered to, and thus a stimulus level delivered by,the probe assembly 50 via an actuator provided on the probe assembly 50(remote of the control unit 20). To this end, the probe assembly 50 andthe control unit 20 are adapted to allow continuous variation (e.g.,increment or decrement) of the stimulation energy over a series ofdiscrete, sequential steps via manipulation of the probe assembly 50actuator. Further, the control unit 20 processes information (e.g.,patient response) received from sensing probes 52 resulting fromdelivered stimulation.

Using the sensing probes 52, the system 10 performs monitoring basedupon recorded EMG activity in response to an electrical current energydelivered by the probe assembly 50 and/or physical manipulation oftissue. With the one embodiment of FIG. 1, the console 30 and thepatient interface module 40 are provided as separate components,communicatively coupled by a cable 54. Alternatively, a wireless linkcan be employed. Further, the console 30 and the patient interfacemodule 40 can be provided as a single device. In basic terms, however,the patient interface module 40 serves to promote easy connection ofstimulus/sensory components (such as the probe assembly 50 and sensingprobes 52), as well as to manage incoming and outgoing electricalsignals. The console 30, in turn, interprets incoming signals (e.g.,impulses sensed by sensing probes 52), displays information desired by auser, provides audible feedback of signals, presents a user interface(such as by including, for example, a touch screen), and delivers astimulation energy to the probe assembly 50 pursuant to control signalsfrom the probe assembly 50 (via connection to the patient interfacemodule 40), as well as other tasks as desired.

As previously described, the patient interface module 40 communicateswith the console 30 through the cable 54 information to and from theprobe assembly 50, as well as information from the sensing probes 52. Ineffect, the patient interface module 40 serves to connect the patient(not shown) to the console 30. To this end, and in one embodiment, thepatient interface module 40 includes one or more (preferably eight)sensory inputs 56, such as pairs of electrode inputs electricallycoupled to receive signals from the sensing probes 52 (referencedgenerally in FIG. 1). In addition, the patient interface module 40provides a stimulator input port 58 (referenced generally in FIG. 1) anda stimulator output port 60 (referenced generally in FIG. 1). Thestimulator input port 58 receives control signals from the probeassembly 50 relating to desired stimulation levels and/or otheractivities, whereas the stimulator output port 60 facilitates deliveryof stimulation energy to the probe assembly 50. The patient interfacemodule 40 can further provide additional component port(s), such as aground (or return electrode) jack, auxiliary ports for additionalstimulator probe assemblies, etc.

The control unit 20, and in particular the console 30 and the patientinterface module 40, are akin in several respects to availablemonitoring systems, such as the NIM-Response™ Nerve Integrity Monitor,available from Medtronic Xomed of Jacksonville, Fla. For example, thetouch screen capabilities provided by the NIM-Response™ Nerve IntegrityMonitor can be incorporated into the control unit 20. In addition,however, the system 10 employs a signal processing module 70, whichperforms signal processing techniques that classifies input signalsreceived from sensing probes 52 and delivers an output signal regardingnerve monitoring during operation of electrosurgical unit 12. Inparticular, the signal processing module 70 can provide an indication oflow EMG activity (including no EMG activity) or high EMG activity duringoperation of the electrosurgical unit 12. Additionally, the signalprocessing module 70 can selectively mute one or more channels ofinformation provided from the sensing probes 52 to the sensory inputports 56, block a direct current (DC) component or low frequency noisein signals received and recover EMG data.

The sensing probes 52 are coupled to the patient (e.g., selected tissue)to provide signals to the signal processing module 70. In oneembodiment, the plurality of probes 52 includes eight probes that areelectronically coupled to sensory inputs 56. In normal operation, theprobes 52 sense electrical signals from the patient and send the signalsto signal processing module 70. These signals include an electricalimpulse from patient tissue, which is indicative of EMG activity in thepatient. However, several conditions can introduce noise into probes 52and thus corrupt signals provided to the signal processing module 70.For example, the current generated by ESU 12 creates noise that isdetected by one or more of the probes 52.

Each of the plurality of probes 52 constitutes a separate channel thatcan be independently processed in signal processing module 70, asdiscussed below. For example, if a total of eight sensing probes areused, eight separate channels can be independently processed by signalprocessing module 70. To this end, the signal processing module 70includes components that classify signals received from the sensingprobes 52 and allows a surgeon to maintain monitoring of nerve activityfor one or more channels during an electrosurgical procedure. Theclassification can be a low level of EMG activity (including zero) or ahigh level of EMG activity.

In one embodiment, each of the plurality of probes 52 includes afront-end filter, for example filter 72, that can be utilized to filterthe fundamental frequency generated by the ESU 12. Alternatively, asingle front-end filter can be provided to filter the signals receivedfrom each of the sensing plurality of probes 52. Through analysis ofoperation of ESU 12 and/or signals generated by ESU 12, it can bedetermined what components are present during operation of the ESU 12.In one embodiment, the ESU 12 generates a 29 kHz pulsed 500 kHzradiofrequency signal as well as additional harmonics. Filter 72 can beadjusted so as optimize filtering of the signal generated by ESU 12 andthus reduce noise provided to signal processing module 70.

FIG. 2 is a schematic block diagram of signal processing module 70,which receives input signals 110 and processes the signals to produceoutput signals 120 indicative of a level of EMG activity in the inputsignals 110. The output signals 120 can further be supplemented byadditional indications, for example an artifact detection condition, arecovered EMG signal, etc. Signals from the sensing probes 52 (FIG. 1)are received in signal processing module 70 through an input module 122.Illustratively, input module 122 can associate signals with a particularprobe (i.e., channel) that is used by other modules within signalprocessing module 70. In addition, input module 122 can include ananalog-to-digital convert (ADC), which samples the signals received at aspecified rate in order to convert the signals from an analog form to adigital form, as discussed in more detail below. In addition to inputmodule 122, signal processing module 70 includes an output module 124that provides the output signals 120, for example to console 30 (FIG.1). Between input module 122 and output 124 are a plurality of modulesfor detecting conditions in signals received by the input module 122 andproviding a corresponding response to output module 124 such that nerveintegrity monitoring can be maintained during an electrosurgicalprocedure. In particular, signal processing module 70 includes anartifact detection module 126, a DC filter module 128, an EMG detectionmodule 130 and an EMG signal recovery module 132.

FIG. 3 is a flow diagram of a method 200 for front-end processing ofsignals obtained by nerve integrity monitoring system 10 and inparticular sensing probes 52. At step 202, a signal is obtained by asensing probe, for example one or more of probes 52. The signal isindicative of both ESU data (as caused by operation of ESU 12) as wellas EMG activity (as caused by nerve potential from the patient). At step204, the fundamental frequency of ESU data is filtered. This filteringcan be performed by filter 72 at probe 52 (FIG. 1), for example. Thefiltered signal is then sent to input module 122 of signal processingmodule 70.

As discussed above, the input module 122 includes an ADC operating at asampling rate to process signals received from the sensing probes 52. Toprevent aliasing, the input module 122, at step 206, oversamples thesignal from probes 52. Since electrosurgical unit 12 generates noisehaving a wide range of frequencies, oversampling can be used to preventaliasing in the signal received. The oversampling rate can be severaltimes greater than a sampling rate of the ADC. In one embodiment, theoversampling rate can be 128 times the sampling rate. At step 208, thesignal can be downsampled using a decimation filter to convert theanalog signal sensed at the probes 52 to a digital signal. The digitalsignal is output at step 210. In one example, the ADC samples the signalat a rate of 16 kHz. If the signal is oversampled in step 206 at a rateof 128 times the sampling rate, or 2.048 MHz aliasing can be preventedin component frequencies less than 1.024 MHz and ESU signals greaterthan 8 kHz should not be present in the digital signal output at step210. The digital signal can be sent to artifact detection module 126, DCfilter module 128, EMG detection module 130 and/or EMG signal recoverymodule 132. As discussed below, these modules can process the digitalsignal to detect conditions that are provided to output module 124.

FIG. 4 is a flow diagram of a method 250 for detecting an artifact inone or more channels provided to signal processing module 70, asperformed by artifact detection module 126. The artifact detectionmodule 126 can aid in situations for detecting artifacts that can becaused be metal surgical instruments contacting tissue and/or situationswhere two or more surgical instruments contact each other.

The metal-to-metal (or metal to patient) artifact can be produced wheninstruments with different static electrical charge contact each otheror the patient causing a current to flow as the charge is equalized. Thecharge transfer is by a spark which contains broad band noise spectrumincluding high frequency far above EMG. The night frequency shows up onthe monitor as a fast vertical response not possible to be EMG. Thisoften is on multiple channels at the same time needing filtering.

If a signal for a channel is likely an artifact, the channel can bemuted independent of other channels so as to prevent an indication of afalse positive of EMG activity. At step 252, the digital signalgenerated by method 200 (FIG. 3) is received into artifact detectionmodule 126 from input module 122. At step 254, a high pass filter isapplied to the digital signal with a stop band having a range thatexcludes EMG data. In one example, EMG activity is determined to be in arange from 0 to 3.5 kHz and thus the stop band applied is from 0 to 3.5kHz. The resulting signal is a band limited signal that can further beprocessed to determine if the channel associated with the signal shouldbe muted.

At step 256, the power of the band limited signal is estimated bysquaring the signal and finding a mean over a sample buffer. The buffercan be any size and in one example includes 80 samples, constituting 5milliseconds of data. The mean of the power estimate can then befiltered with an averaging infinite impulse response at step 258. In oneembodiment, the average can include 50% old data and 50% new data. Atstep 260, the filtered average can be compared with a threshold. Ifdesired, hysteresis can be employed in the threshold comparison. As afunction of the comparison, the channel can selectively be muted (i.e.,suppressed) at step 262. An indication that an artifact has beendetected can be output to output module 124. This indication can then berelayed to the surgeon, for example through monitor 32. Thus, a falsepositive can be avoided and the surgeon is not erroneously alerted toEMG activity.

FIG. 5 is a flow diagram of a method 300 for filtering a DC componentfrom data input into the signal processing module 70. At step 302, themean of the signal generated by method 200 (FIG. 3) is obtained. A lowpass infinite impulse response filter is then used, at step 304, tofilter a mean of the signal and block DC in the signal. One examplefilter uses the following equation:y[n]=x[n]−x[n−1]+a·y[n−1],

where x[n] is the input signal (received from input module 122), y[n] isthe output signal and a is a constant. If desired, the value of a can beadjusted to block low frequency components of the signal as well. Afterapplication of the filter, the DC component of the signal is blocked.Then, the blocked DC signal is output at step 306.

FIG. 6 is a flow diagram of a method utilized by EMG detection module130 for detecting a level of EMG activity within a noisy environment,for example noise caused by an electrosurgical procedure. To detect alevel of EMG activity, an autocovariance method is utilized to determinethe presence of a high level of EMG activity. If a high level of EMGactivity is detected during an electrosurgical procedure, the surgeoncan be alerted. Method 350 begins at 352 where a sample of the signal isobtained from method 300 (FIG. 5). The energy of the sample is thenestimated at step 354. The energy level is then compared to a thresholdat step 356. Based on this comparison, a determination is made at step358 as to whether the sample contains sufficient energy to indicate thepresence of a high level of EMG activity. If a probe is poorly connectedor has been disconnected from the patient tissue, the resulting signalwill have limited energy and thus a low level of EMG activity will beprovided to output module 124.

The autocovariance of the signal is calculated at step 360. As is known,the autocovariance is a coefficient that can be determined based ontime-shifted observations of the signal as a function of a lag betweenobservations. By analysis of EMG data, it has been determined that EMGdata is highly correlated. Thus, highly correlated data can indicate ahigh level of EMG activity. At step 362, a mean for the autocovariancesignal can be calculated for all or a selected number of lags. Thecalculated means are then compared to a threshold at step 364. If themeans exceed the threshold, an indication of presence of a high level ofEMG activity is provided at step 366.

Several adjustments can be made to method 350 to improve robustness. Forexample, a window function (e.g., a Bartlett window) can be applied tothe samples obtained to reduce end effects that can be caused bycalculating the autocovariance coefficients over a finite number ofsamples. Furthermore, a level detector can be utilized to determine ifthe signal is close to a rail (e.g., an upper or lower voltage level) ofthe ADC of input module 122. In this case, no EMG activity will bereported. Yet another adjustment can be made to the DC blocking filter.For example, the filter can be made more aggressive to attenuate lowfrequency data. Furthermore, multiple data buffers (e.g., four) can beused to improve the autocovariance results. If desired, theautocovariance calculation can be spread out the computing performance.Additionally, before comparing data to the EMG threshold, the mean ofthe square of selected coefficients can be used as a filter input toreduce noise and smooth data. It is worth noting that other methods ofclassification can also be used. For example, autocorrelation, wavelets,sigmoid functions, etc. can all be used to classify a noisy signal ascontaining EMG and/or detect EMG activity in a signal.

FIG. 7 is a flow diagram of a method 400 of applying an adaptive filterin an EMG recovery technique. Method 400 begins at step 402, where theinput signal is obtained from the input module 122. At step 404, anadaptive filter is applied to the signal to block noise generated by theESU 12. The filter can be reference based or non-reference based usingvarious techniques. Once the noise generated by the ESU is filtered, asignal indicative of the EMG activity is output at step 406.

Various adaptive filters and adaptive filtering techniques can beemployed in method 400. When using a reference based filter, one of thesensing probes 52 can be utilized to estimate the noise created by theelectrosurgical unit 12 in the input signal. The data from the referenceprobe serves as the noise estimate to the adaptive filter. For example,a least mean square algorithm, a normalized least mean square algorithm,or a recursive algorithm can be used as a referenced based adaptivefilter. These algorithms can be adjusted to vary a number of terms usedand how data in the filter is processed to recover EMG data in a noisysignal created by electrosurgical unit 12.

Additionally, non-reference based adaptive algorithms can be used inmethod 400 to recover EMG data. Example filters include Kalman filtersand H-infinity filters. These filters can also be adjusted as desired toproduce a recovered EMG signal.

Although the present disclosure has been described with reference topreferred embodiments, workers skilled in the art will recognize thatchanges can be made in form and detail without departing from the spiritand scope of the present disclosure.

What is claimed is:
 1. A surgical method, comprising: attaching aplurality of sensing probes to a tissue of a patient, the plurality ofsensing probes electrically coupled to an input module; operating anelectrosurgical unit proximate the plurality of sensing probes;simultaneously receiving first and second input signals from theplurality of sensing probes, the first input signal of the plurality ofinput signals indicative of electromyographic (EMG) activity of thepatient and operation of the electrosurgical unit and the second inputsignal of the plurality of input signals indicative of an artifact ofmetal-to-metal contact; classifying the first input signal received fromthe plurality of sensing probes as a function of a level of EMG activityreceived from the patient with an EMG detection module; and providing anoutput signal indicative of the level of EMG activity with an outputmodule; detecting for artifact in the second input signal with anartifact detection module; and providing a first indication to suppressthe second input signal at the output module when artifact is detectedin the second input signal.
 2. The method of claim 1 wherein theartifact is detected based on an of power in the second input signal. 3.The method of claim 1 and further comprising: suppressing a directcurrent component or low frequency noise in the second input signalhaving a frequency lower than a threshold.
 4. The method of claim 1 andfurther comprising: comparing a level of EMG activity in the pluralityof input signals to a threshold and providing second indication of thecomparison.
 5. The method of claim 4 and further comprising: estimatinga level of energy in the plurality of input signals to determine if thelevel of EMG activity is greater than an energy level threshold.
 6. Themethod of claim 4 and further comprising: calculating an autocovarianceor autocorrelation of the plurality of input signals to detect the levelof EMG activity.
 7. The method of claim 6 wherein the artifact isdetected based on energy in the plurality of input signals and theautocovariance or autocorrelation.
 8. The method of claim 5 and furthercomprising: classifying the plurality of input signals as EMG activitybased on a wavelets or sigmoid classification function.
 9. The method ofclaim 6 wherein calculating the autocovariance or autocorrelation of theplurality of input signals includes using multiple samples of theplurality of input signals.
 10. The method of claim 1 and furthercomprising: filtering noise created by the electrosurgical unit andproviding an EMG indication of EMG activity in the patient.
 11. Themethod of claim 8 and further comprising: using a reference probe toestimate the noise created by the electrosurgical unit.
 12. The methodof claim 1 and further comprising: filtering a fundamental frequency ofa noise signal generated by the electrosurgical unit.